The integration of camera systems into devices has created a large variety of systems of different shapes, sizes, and functionalities. These devices, for example, include smart phones, surveillance cameras, wearable cameras, camcorders, digital single-lens reflex cameras, mirror-less cameras, and tablets. The integration of the camera systems may require a tradeoff in image quality, device size, and/or costs. Usually, a desire for improved image quality remains same although the systems and image/video capturing platforms may change. For example, digital images and video frames may have certain issues with image quality related to sharpness, noise, distortions, artifacts, and/or chromatic aberration.
In image processing and photography, aliasing is an effect that causes different signals to become indistinguishable or aliases of one another when sampled. A digital image sampling system may suffer from aliasing when the input signal's frequency is higher than the Nyquist frequency of the system. For a digital image, distortions and artifacts may result, when the digital image may be reconstructed from samples different from the original scene. For multi-channel images, such as digital color images, reconstructed by colored image sampling systems, colors that may be different from those in the actual scenes may appear in some regions of reconstructed color images due to aliasing. Such false colors may be more noticeable to a human visual system, and usually referred to as “color aliasing”.
Typically, a multi-channel image is obtained via a single image sensor overlaid with a spatial color filter array (CFA), which is a mosaic of tiny color filters placed over each pixel. The most famous CFA pattern is the Bayer pattern that involves red, green, and blue filters. Imaging sensors equipped with the Bayer CFA pattern are often called Bayer sensors. Imaging sensors equipped with a CFA tend to suffer from color aliasing from processing. In order to suppress color aliasing, filters such as optical anti-aliasing filters are generally applied in front of a color-imaging sensor. However, these filters result in a tradeoff in picture quality as the filters reduce the overall sharpness of a captured image.
In order to increase the sharpness of captured images and reduce production cost, more and more image sensors are manufactured without optical anti-aliasing filters. Thus, a need still remains for an image processing system that can deliver sharpness in picture quality while still removing and suppressing the disadvantages of color aliasing. In view of the increasing demand for providing higher resolution images and videos, it is increasingly critical that answers or solutions need be found to these problems.
The aliased colors in the one or more regions of the multi-channel image may be present in different patterns, such as a circular pattern, corner circular pattern, and/or moiré pattern, based on a change in pixel values in a color-aliased region. Thus, there is a need for an efficient and a robust anti-aliasing technique and/or system that may suppress the aliased colors, which are in specific signal patterns, in one or more color-aliased regions in the multi-channel image.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.